Mathematics, B.S. - Statistics

Mathematics |View/Print PDF

The graduation requirements are listed below. In addition, students select free electives to reach 120 credits overall required for the degree.  The department website provides an overview of the program, admission requirements for the major (when applicable), faculty biographies, learning outcomes, and careers:


All bachelor’s degree programs include liberal education (LEP) and writing (W) course requirements. To review more detailed information, please visit General Education (LEP) Degree Requirements.   


(*) When up to three courses in the major/cognate may also satisfy LEP requirements, they are recommended below; only two courses within Explorations (T2) may be fulfilled by courses in the same subject.  

In those mathematics courses which the student applies toward the major in mathematics, he/she must have a GPA of 2.0 and, at most, one grade below 'C-'.

MAT 115 — Introduction to Modern Mathematics (‘C-’ or better)
MAT 150 — Calculus I (‘C-’ or better) (T1QR)*
MAT 151 — Calculus II (‘C-’ or better)
MAT 250 — Foundations of Mathematics: An Introduction (‘C-’ or better)
MAT 252 — Calculus III (‘C-’ or better)
MAT 320 — Mathematical Statistics I (‘C-’ or better)
MAT 321 — Mathematical Statistics (‘C-’ or better)
MAT 326 — Regression Analysis (‘C-’ or better)
MAT 372 — Linear Algebra (‘C-’ or better)
MAT 446 — Advanced Calculus with Applications (‘C-’ or better) or MAT 450 — Analysis (‘C-’ or better)
MAT 488 — Seminar in Mathematical Modeling (‘C-’ or better)
MAT 491 — Mathematics Capstone I (‘C-’ or better)
MAT 492 — Mathematics Capstone II (‘C-’ or better)

Select one from (‘C-’ or better) :
MAT 328 — Time Series Analysis 
MAT 329 — Bayesian Analysis and Decision Making
MAT 428 — Mathematical Foundations in Machine Learning
MAT 429 — Modern Nonparametric Statistics

With the approval of a departmental advisor, select two courses from (‘C-’ or better): 
MAT 245 — Differential Equations
MAT 300 — History of Mathematics
MAT 322 — Numerical Analysis I
MAT 325 — Design of Experiments
MAT 328 — Time Series Analysis
MAT 329 — Bayesian Analysis and Decision Making
MAT 360 — Foundations of Geometry
MAT 370 — Number Theory
MAT 373 — Modern Algebra
MAT 375 — Abstract Algebra
MAT 376 — Abstract Algebra II
MAT 378 — Discrete Mathematics
MAT 398 — Special Topics in Mathematics
MAT 428 — Mathematical Foundations in Machine Learning
MAT 429 — Modern Nonparametric Statistics
MAT 446 — Advanced Calculus with Applications 
MAT 450 — Analysis
MAT 480 — Topology
MAT 498 — Seminar in Mathematics


DSC 100 — Data Science I
DSC 101 — Data Science II